OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
RAG & Knowledge Systems

Embedbase

Embedbase is an API and JavaScript SDK for adding information to datasets, running semantic searches over them, and generating text with retrieved results as model context.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Embedbase — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Embedbase when an application needs a managed API and JavaScript client to connect dataset search with language-model generation.

Who it is for

  • Developers building applications that need semantic search over their own information.
  • Teams adding retrieved knowledge to LLM-powered question-and-answer or chat experiences.
  • Builders creating recommendations based on related records in a dataset.

Documented uses

  • Search a dataset for information relevant to a natural-language question.
  • Pass retrieved records alongside a question to a language model for text generation.
  • Use semantic retrieval as a component in documentation search or recommendation features.
Before you choose

Requirements & limits

The README documents a hosted version and shows the SDK initialized with a token obtained through the hosted app. It also identifies the project as open source under the MIT license.

  • The documented JavaScript client exposes dataset search and text generation in one workflow.
  • The repository describes use of vector databases and LLMs without requiring the developer to host them.
  • The README example uses a predefined dataset; dataset creation and population are not covered in that example.
  • The project is TypeScript-based and distributed under the MIT license.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Use dataset search results as LLM prompt context

Retrieve relevant information from a dataset and include it with a question in generated text.

Before you start

  • Node.js project able to install the embedbase-js package
  • Embedbase API URL and token obtained through the hosted app
  • A predefined dataset
  • A selected supported language model

Steps

  1. Install the SDK with the command below and import createClient.

    Documented command
    npm i embedbase-js

  2. Create a client using https://api.embedbase.xyz and the Embedbase token.

  3. Search a named dataset with .dataset(name).search(question).

  4. Combine the returned data with the question and pass it to .useModel(model).generateText(...).

Expected outcome

Generated text based on the question and information returned by semantic search.

Guide limitations

  • The README example assumes a predefined dataset and a token; it does not document how to create or populate that dataset in this flow.
  • The documented example uses OpenAI gpt-3.5-turbo; the README mentions 9+ LLMs but does not enumerate them.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
different-ai/embedbase · Official homepage title · retained in its source language
How we review sourcesSuggest a correction